Glaucoma is the second leading cause of blindness in the world. [1] The loss in vision is irreversible, and recent estimates based on meta-analysis of population-based studies indicate that in 2010 there were 44.7 million people with open-angle glaucoma (OAG) worldwide, and 2.8 million people in the United States. [2] These numbers are projected to reach 58.6 million worldwide [2] and 3.4 million in the United States by 2020. [3]
The current standard for the diagnosis of glaucoma is based on the presence of typical structural changes with corresponding functional deficits. However, studies on early changes in glaucoma have shown that structural changes indicated by significant loss of retinal ganglion cells and their axons may precede detection of functional deficit by currently available standard automated perimetry (SAP) methods. [4, 5] In addition, SAP is subject to fluctuation even in clinically stable glaucoma.
Ophthalmoscopy and optic disc photography traditionally have been used as primary methods for structural assessment in glaucoma. However, substantial interobserver variability and low to medium interobserver agreement in detecting subtle changes makes ophthalmoscopy or fundus photography alone poor methods for detecting changes indicative of early glaucoma or glaucomatous progression. [6, 7]
In recent years, and with the advent and continuous improvement of imaging devices such as scanning laser ophthalmoscopy (SLO), scanning laser polarimetry (SLP), and time and spectral-domain optical coherence tomography (SD-OCT), a great deal of effort has been invested in identifying quantifiable parameters for objective assessment of structural glaucomatous damage. Of these devices, SD-OCT has rapidly become one of the most widely used technologies in daily clinic due to its high image resolution and measurement precision. The caveat, however, is that the clinician is presented with an array of quantitative information to mentally process as part of the diagnosis process. The multitude of parameters—most of which are highly correlated and are redundant to some extent—oftentimes renders the interpretation process difficult, particularly when structural changes conflict in their results.
For example, optic nerve head (ONH) parameters may appear normal but retinal nerve fiber layer (RNFL) measurements may appear abnormal. One way of circumventing this issue would be to use a multivariable model that reduces the number of structural parameters provided by the OCT output into a set of fewer parameters containing the most useful and relevant information from the original set while also explaining a majority of variability in the original dataset. Earlier studies have combined structural parameters measured by one or several of the devices listed above to assess the glaucoma diagnostic ability using various methods such as machine learning classifiers, linear discriminant functions (LDF), and principal component analysis (PCA). [8-12] In addition, three recent studies used linear discriminant analysis to assess diagnostic ability of combined structural parameters measured SD-OCT. One of these studies combined ONH and peripapillary RNFL parameters,[13] whereas the two others used a combination of ONH, peripapillary RNFL, and ganglion cell of complex (GCC) parameters,[14, 15] which is anatomically different from ganglion cell inner-plexiform layer (GCIPL). [16] In view of these studies however, a need still exists for predicting early onset glaucoma in patients.
In view of the existing glaucoma diagnosis methods and studies described above, it can be appreciated that the diagnosis of glaucoma in the early stages is challenging. One of the major obstacles in preventing glaucoma blindness is the failure to identify individuals with the condition in the early stages, when changes are not distinct. Waiting until significant visual function loss has occurred means that 30-50% of the cells have died and cannot be regenerated.
In view of the shortcomings of the existing glaucoma diagnosis methods and studies described above, and further in view of the high number of people affected by glaucoma, a need exists for methods, systems, and computer readable media for predicting early onset glaucoma using a combination of structural parameters measured via SD-OCT.